US6895103B2 - Method for automatically locating eyes in an image - Google Patents
Method for automatically locating eyes in an image Download PDFInfo
- Publication number
- US6895103B2 US6895103B2 US09/884,373 US88437301A US6895103B2 US 6895103 B2 US6895103 B2 US 6895103B2 US 88437301 A US88437301 A US 88437301A US 6895103 B2 US6895103 B2 US 6895103B2
- Authority
- US
- United States
- Prior art keywords
- eye
- iris
- pixel
- template
- region
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Expired - Lifetime, expires
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Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/16—Human faces, e.g. facial parts, sketches or expressions
- G06V40/168—Feature extraction; Face representation
Landscapes
- Engineering & Computer Science (AREA)
- Oral & Maxillofacial Surgery (AREA)
- Health & Medical Sciences (AREA)
- Physics & Mathematics (AREA)
- General Health & Medical Sciences (AREA)
- Human Computer Interaction (AREA)
- Computer Vision & Pattern Recognition (AREA)
- General Physics & Mathematics (AREA)
- Multimedia (AREA)
- Theoretical Computer Science (AREA)
- Image Analysis (AREA)
- Image Processing (AREA)
- Collating Specific Patterns (AREA)
Abstract
Description
where P(iris|I) is the conditional probability that a given pixel intensity belongs to an iris; P(I|iris) is the conditional probability that a given iris pixel has a specific intensity I; P(iris) is the probability of the occurrence of an iris in the face oval region; P(I|noniris) is the conditional probability that a given non-iris pixel has a specific intensity I, and P(noniris) is the probability of the occurrence of a non-iris pixel in the face oval region. The Bayes model further applies the probability of the occurrence of an iris in a face oval region and the probability of the occurrence of a non-iris pixel in the face oval region. Using a probability analysis based on the Bayes model, a pixel is classified as an iris if the conditional probability that a pixel having a given red intensity belongs to an iris is greater than, for example, 0.05.
PARTS LIST |
100 | image source |
102 | image processor |
104 | image display |
106 | data and command entry device |
107 | computer readable storage medium |
108 | data and command control device |
109 | output device |
201 | color histogram equilization block |
202 | skin color detection block |
204 | oval region extraction block |
206 | iris color pixel detection block |
208 | group iris pixels into cluster step |
210 | find cluster center block |
212 | oval division block |
214 | initial estimate of eye position block |
216 | locate final eye position block |
226 | Iris color/Bayes Model Training block |
300 | oval_top |
302 | oval_bottom |
304 | oval_left |
306 | oval_right |
308 | oval_center_row |
310 | oval_center_column |
402 | conditional probability that a given pixel is an iris pixel |
404 | conditional probability that a given pixel is a non-iris pixel |
502 | iris pixels |
504 | non-iris pixels |
506 | probability of iris pixel in face region |
508 | probability of non-iris pixel in face region |
510 | probability a pixel intensity is iris |
600 | center position |
602 | pixel cluster |
604 | left half region |
606 | right half region |
S2 | initiate process |
S4 | estimate size of eyes |
S6 | eye template |
S8 | form search window |
S10 | determine center pixel of eye |
S10a | initialize zone base correlation |
S10b | retrieve and normalize template |
S10c | extract block |
S10d | normalize extracted block |
S10e | compute cross correlation |
S10f | perform cross correlation with entire template |
S10h | store correlation value in buffer |
S10i | set across correlation to zero |
S10j | vary template |
S10k | select highest correlation value |
S10l | continue |
S12 | select pixel at highest correlation point |
S14 | local maximum correlation |
S16 | store peak locations in buffer |
S18 | form pixel combinations |
S20 | pair with highest cumulative score |
S22 | final eye locations |
Claims (15)
Priority Applications (3)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US09/884,373 US6895103B2 (en) | 2001-06-19 | 2001-06-19 | Method for automatically locating eyes in an image |
JP2002162530A JP2003030667A (en) | 2001-06-19 | 2002-06-04 | Method for automatically locating eyes in image |
EP02077231A EP1271394A3 (en) | 2001-06-19 | 2002-06-07 | Method for automatically locating eyes in an image |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US09/884,373 US6895103B2 (en) | 2001-06-19 | 2001-06-19 | Method for automatically locating eyes in an image |
Publications (2)
Publication Number | Publication Date |
---|---|
US20030016846A1 US20030016846A1 (en) | 2003-01-23 |
US6895103B2 true US6895103B2 (en) | 2005-05-17 |
Family
ID=25384476
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US09/884,373 Expired - Lifetime US6895103B2 (en) | 2001-06-19 | 2001-06-19 | Method for automatically locating eyes in an image |
Country Status (3)
Country | Link |
---|---|
US (1) | US6895103B2 (en) |
EP (1) | EP1271394A3 (en) |
JP (1) | JP2003030667A (en) |
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US20030016846A1 (en) | 2003-01-23 |
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